System and method for detecting change over in manufacturing field

ABSTRACT

Example implementations described herein are directed to a change over detection system and method. Such example implementations facilitate updating the information of the production line in an Internet of Things (IoT) system to the latest version. Through the example implementations described herein, device rearrangement in manufacturing field can thereby be detected.

BACKGROUND Field

The present disclosure is generally related to manufacturing systems,and more specifically, for systems and methods to detect change over ina manufacturing field

Related Art

With applying an IoT (Internet of Thing) solution to the manufacturingfield, an IT (information technology) server collects the data thatrepresents the behavior of manufacturing machines on the field via PLC(Programmable Logic Controller). Many productive lines that involvemultiple PLCs produce many kinds of product types in a mixed productionmanner. Product types can change frequently according to the productionplan. When the product type is changed, the PLC obtains differentsettings from the MES (Manufacturing Execution System), manualoperation, and so on, and the metrics corresponding to the dataretrieved from the PLC may also become different. Such events are knownas a change-over. Further, the management of the manufacturing field andIT server are siloed from each other, so engineers of the IT servercannot obtain the change-over notification. Thus, there is a need todetect the change-over event from the IT server side.

SUMMARY

Aspects of the present disclosure can include a method, which caninvolve, for a detection of a stoppage and a restart of the PLC within athreshold period of time, retrieving first metrics from the PLC frombefore the stoppage and second metrics from the PLC from after therestart; extracting first features from the first metrics and secondfeatures from the second metrics; and for a difference between the firstfeatures and the second features exceeding a threshold, transmitting anotification to an asset management server indicative of a changeover ofan asset of the PLC.

Aspects of the present disclosure can include a non-transitory computerreadable medium, storing instructions which can involve, for a detectionof a stoppage and a restart of the PLC within a threshold period oftime, retrieving first metrics from the PLC from before the stoppage andsecond metrics from the PLC from after the restart; extracting firstfeatures from the first metrics and second features from the secondmetrics; and for a difference between the first features and the secondfeatures exceeding a threshold, transmitting a notification to an assetmanagement server indicative of a changeover of an asset of the PLC.

Aspects of the present disclosure can include a system which caninvolve, for a detection of a stoppage and a restart of the PLC within athreshold period of time, means for retrieving first metrics from thePLC from before the stoppage and second metrics from the PLC from afterthe restart; means for extracting first features from the first metricsand second features from the second metrics; and for a differencebetween the first features and the second features exceeding athreshold, means for transmitting a notification to an asset managementserver indicative of a changeover of an asset of the PLC.

Aspects of the present disclosure can include an apparatus which caninvolve, a processor configured to, for a detection of a stoppage and arestart of the PLC within a threshold period of time, retrieve firstmetrics from the PLC from before the stoppage and second metrics fromthe PLC from after the restart; extract first features from the firstmetrics and second features from the second metrics; and for adifference between the first features and the second features exceedinga threshold, transmit a notification to an asset management serverindicative of a changeover of an asset of the PLC.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a system diagram of an example change over detectionsystem in accordance with an example implementation.

FIG. 2 illustrates a data flow in the change over detection server, inaccordance with an example implementation.

FIG. 3 illustrates an example of the asset repository in accordance withan example implementation.

FIG. 4 illustrates an example of the data storage in accordance with anexample implementation.

FIG. 5 illustrates an example of the recent PLC running information inaccordance with an example implementation.

FIG. 6(a) illustrates a flowchart on an example of the behavior basechange over detector in accordance with an example implementation.

FIG. 6(b) illustrates an example of feature extraction in accordancewith an example implementation.

FIG. 7 illustrates an example of the recent operation signals inaccordance with an example implementation.

FIG. 8(a) illustrates a flowchart of an example of the learning changeover, in accordance with an example implementation.

FIG. 8(b) illustrates an example of extraction of data from the flow ofFIG. 8(a).

FIG. 9 illustrates a flowchart of an example of the signal monitoringbase change over detector, in accordance with an example implementation.

FIG. 10 illustrates a system diagram of an example change over detectionsystem in accordance with another example implementation.

FIG. 11 illustrates an example computing environment with an examplecomputer device suitable for use in some example implementations.

DETAILED DESCRIPTION

The following detailed description provides details of the figures andexample implementations of the present application. Reference numeralsand descriptions of redundant elements between figures are omitted forclarity. Terms used throughout the description are provided as examplesand are not intended to be limiting. For example, the use of the term“automatic” may involve fully automatic or semi-automaticimplementations involving user or administrator control over certainaspects of the implementation, depending on the desired implementationof one of ordinary skill in the art practicing implementations of thepresent application. Selection can be conducted by a user through a userinterface or other input means, or can be implemented through a desiredalgorithm. Example implementations as described herein can be utilizedeither singularly or in combination and the functionality of the exampleimplementations can be implemented through any means according to thedesired implementations.

Example implementations described herein are directed to a change overdetection method and system.

FIG. 1 illustrates a system diagram of an example change over detectionsystem in accordance with an example implementation. The example systeminvolves at least one Manufacturing Field System 100, Asset ManagementServer 170, IT Server 160 and Change Over Detection Server 180.

The Manufacturing Field System 100 involves an IT Network 110, ControlNetwork 111, one or more environmental devices 120, a MES 130, one ormore PLCs 140, 141, 142, and one or more Assets 150, 151, 152. The ITNetwork 110 facilitates connections between one or more environmentaldevices 120, Change Over Detection Server 180, and IT Server 160. TheControl Network 111 facilitates connections between MES 130, one or morePLCs 140, 141, 142, one or more Assets 150, 151, 152, Change OverDetection Server 180, and IT Server 160. Environmental devices 120 caninvolve various devices such as one or more Cameras 121, Andons 122 andSensors 123.

The IT Server 160 involves one or more data retrievers 161, data storage162 and one or more solution applications 163. The IT server 160 isconnected to the control network 111. The data retriever 161 acquiresdata from MES 130 and PLCs 140-142, annotates metadata onto the acquireddata, and stores the annotated data into data storage 162. For example,if the acquired data is “0.71, 0.69” and it is acquired from PLC-A, thedata retriever 161 creates annotated data such as “PLC-A, screw #1torque, 0.71, screw #2 torque, 0.69”. The solution application 163 readsthe annotated data from the data storage 162, conducts analytics andprovides the analytics results to the manufacturing manager 164.

The asset management server 170 involves an asset manager 171 and assetrepository 172. The asset manager 171 provides a configuration of thedata retriever 161 based on the asset repository 172. The configurationindicates how to annotate metadata onto the acquired data.

The change over detection server 180 involves a behavior base changeover detector 181, an operation signal tracker 182, a learning changeover 183, a recent operation signals 184, a change over detecting model185 and a signal monitoring base change over detector 186.

FIG. 2 illustrates a data flow in the change over detection server 180,in accordance with an example implementation. In an exampleimplementation, at 201 the change over detection server 180 acquiresrecent PLC running information 182 from environmental devices 120 andMES 130. Simultaneously, at 202 the operation signal tracker monitorsone or more operation signals and stores the monitored result to therecent operation signals 184.

Then, the behavior base change over detector 181 evaluates if a changeover has occurred by using acquired data 201, asset repository 172 anddata storage 162. The behavior base change over detector 181 sends achange over notification 202 to the asset manager 171 and learningchange over 183 if a change over has occurred. Then the learning changeover 183 executes a learning process through using the change overnotification 202 and recent operation signals 184, and reflects theresult to change over detecting model 185.

The signal monitoring base change over detector 186 evaluates if achange over has occurred by using the recent operation signals 184 andthe change over detecting model 185. If a change over has occurred, thesignal monitoring base change over detector 186 sends the change overnotification 203 to the asset manager 171.

FIG. 3 illustrates an example of the asset repository 172 in accordancewith an example implementation. The production model #1 shown in 301 and302 relates PLC #1, #2, and #3 as shown in 310, 311, and 312 withcorresponding manufacturing machines 320, 321, and 322.

FIG. 4 illustrates an example of the data storage 162 in accordance withan example implementation. The data storage 162 stores the data the dataretriever 161 retrieved from MES 130 and one or more PLCs 140, 141, 142in a time series manner. Such data can include the date of the data 401,source PLC 402, metrics number 403, and the value recorded from the PLC404.

Data storage 162 can manage one or more entries 411, 412, 413, 414, 415.For example, entry 411 indicates that at a date of “2019-11-0510:36:01”, from source PLC#1, the value of a metric #1 is 0.765.

FIG. 5 illustrates an example of the recent PLC running information inaccordance with an example implementation. The recent PLC runninginformation 182 stores the information collected from environmentaldevices 120 and MES 130 in a time series manner for a certain duration(e.g. 1 day). Such information can include the date of the information501, the newest status of the PLC 502, the PLC ID 503, and the source ofthe information 504.

Recent PLC running information 182 can manage one or more entries 511,512, 513, 514, 515, 516. For example, entry 511 indicates that on date“2019-11-05 10:37:01”, PLC having the ID of PLC#3 changed status to stopafter the source (Power sensor) indicated that PLC#3 has stopped.Through the PLC running information, the stop and restart of variousPLCs can thereby be known as well as the time that the PLCs stopped andrestarted.

FIG. 6(a) illustrates a flowchart on an example of the behavior basechange over detector 181 in accordance with an example implementation.The change over detector 181 generates and sends the change overnotification 203 by using data from recent PLC running information 182,asset repository 172 and data storage 162. The change over notification203 involves the time when the change over occurred, one or more of theasset repository 172 related information such as production model shownin 301, production line shown in 302, and PLCs shown in 310-312.

The flow is invoked at 600. At 601, the flow retrieves a PLC series asshown at 310-312 related to the current production ID shown in 301 fromthe asset repository 172. At 602, the flow retrieves PLC runninginformation as illustrated at 511-516 and as related to the PLC seriesretrieved at 601 from the recent PLC running information 182. At 603, adetermination is made as to whether the PLC series stopped within acertain short term (e.g. 2 min), then restarted within a certain shortterm (e.g. 2 min). If not (N) then the flow proceeds back to 601.Otherwise (Y) the flow proceeds to 604 as a potential change over mayhave occurred.

At 604, the flow retrieves all values 404 on all metrics 403 on all PLCseries retrieved at 601 within a certain term (e.g. 30 min) before thestoppage evaluated on 603 (hereinafter, previous-values) and within acertain term (e.g. 30 min) after the restart evaluated on 603(hereinafter, after-values) from data storage 162.

At 605, the flow extracts various types of features (e.g. average,median, variance, deviation, etc.) from the previous-values andafter-values retrieved at 604.

At 606, the flow calculates the differentiation (difference) scorebetween the features from previous-values and after-values by using theformula that indicates the higher score as the differentiation of eachfeature, wherein the features having high differentiation appearsconsecutively such as Π[all features] (f(differentiation of feature,number of consecutive high differentiation)), or other formulas that fitthe desired implementation. As for another formula calculationcandidate, the formula calculates the child scores by using featuresfrom each of the PLCs by considering a number of features that have ahigh differentiation (e.g., beyond a threshold), and then calculatesfinal score based on child scores. Through this example implementation,the formula considers situations involving a partial change over thataffects a few PLCs.

At 607, a determination is made as to whether the calculated score at606 is greater than a threshold. If not (N), the flow proceeds to 601.Otherwise (Y), a determination is made that a change over event hasoccurred, and the flow proceeds to 608 to send the change overnotification 203 to asset manager 171 and learning change over 183. FIG.6(b) illustrates an example of a feature extraction for all values inaccordance with an example implementation. The graph of featuresindicates each of the feature values collected from PLCs wherein theareas between vertical dotted lines indicate the feature series from onePLC. Some of the feature values in after-value duration are clearlydifferent from previous-values before the stop duration. Such featureextraction is conducted through the flow of FIG. 6(a).

FIG. 7 illustrates an example of the recent operation signals 184 inaccordance with an example implementation. The recent operation signals184 stores the monitored signal between MES 130 and PLCs 140-142 in atime series manner within a certain duration (e.g. 1 day). Asillustrated in FIG. 7, the recent operation signals 184 can include thedate 701, the source of the operation signal 702, the destination forthe operation signal 703, and the operation signal 704.

Recent operation signals 184 can involve entries 711, 712, 713, 714,715. For example, entry 711 indicates that at date “2019-11-05 9:36:01”,MES transmitted operation signal “038sadbalea3sdafge” to PLC#3.

FIG. 8(a) illustrates a flowchart of an example of the learning changeover 183, in accordance with an example implementation. The learningchange over 183 receives the change over notification 203 from thebehavior base change over detector 181, corresponds the change overnotification 203 with the search signal series from the recent operationsignals 184, and applies re-enforcement learning to the change overdetecting model 185. The change over detecting model 185 accepts one orplural sequential inputs and output one score representing change overpossibility. For example, recurrent neural network (hereinafter, RNN)type logic or other types of learning can be used in accordance with thedesired implementation. An example implementation of RNN can involvereceiving a plurality of consecutive input 712, 713 and 714, and thenproviding an output of the score 0 from 1 that indicates the possibilityof change over.

The flow is invoked at 800. At 801, the flow receives a change overnotification 203 from behavior base change over detector 181. At 802,the flow searches recent operation signals 184 and retrieves the signalseries including one or more signals that relates the change over PLCsand recorded just before the time the change over occurred consecutively(e.g. recorded within 5 min, signal intervals are less than 1 min, andthe last operation signal before change over is included). At 803, theflow applies re-enforcement learning by using signal series retrieved at802 to the change over detecting model 185.

FIG. 8(b) illustrates an example of extraction of data from the flow ofFIG. 8(a) at 802. In the example of FIG. 8(b), the change over occurredat 10:41:45 so the signal series as illustrated in FIG. 8(b) is obtainedbased on the operation data timestamp. An example of re-enforcementlearning uses the signal series that involves Src 702, Dst 703 andOperation Signal 704 and the results that indicates a change over hasbeen occurred or not, then enhance the learning model.

FIG. 9 illustrates a flowchart of an example of the signal monitoringbase change over detector 186, in accordance with an exampleimplementation. The signal monitoring base change over detector 186scrapes the recent operations signals 184 intermittently (e.g. every 30min interval), collects one or more signal series, and evaluates thescore by using the change over detecting model 185 and send change overnotification 203 to the asset manager 171 if the score over threshold.

The flow is invoked at 900. At 901, the flow searches the recentoperation signals 184 and retrieves one or more signal series thatincludes one or more signals recorded consecutively (e.g. recordedwithin 5 min, signal intervals are less than 1 min). At 902, adetermination is made as to whether a signal series is present. If so(Y) then the flow proceeds to 903, otherwise (N) the flow reverts backto 901.

At 903-905, an iterative process is initiated to process each of thesignal series retrieved at 901. At 903, the change over detecting model185 is used wherein each signal series is input to determine an outputmodel score. At 904, a determination is made as to whether the outputmodel score from 903 exceeds a threshold. If not (N), then the iterationproceeds to the next signal series, otherwise (Y) the flow proceeds to905 to send the change over notification 203 to the asset manager 171.

FIG. 10 illustrates a system diagram of an example change over detectionsystem in accordance with another example implementation. In the examplesystem, there are one or more smart PLCs 1000. The smart PLC 1000involves a control program execution environment 1010, IT containerexecution environment 1020, a shared memory 1030 and multiple networkinterfaces (IF) 1041, 1042, 1043.

The control program execution environment 1010 can include controlprogram 1011. The control program 1011 drives at least one asset 150-1,150-2, receive operation signals from MES 130 via the control network111 and shares data regarding asset control status and operation signalsfrom MES 130 via the shared memory 1030.

The IT container execution environment 1020 involves a change overdetection program 1021 and data retrieving program 1022. The change overdetection program 1021 involves the same function as the change overdetection server 180 on FIG. 1. The change over detection program 1021retrieves operation signal from MES 130 and PLC running information viashared memory 1030 from control program 1011. The data retrievingprogram 1022 retrieves PLC control data from the control program 1011via the shared memory 1030 as well as the data retriever 161 on FIG. 1.

FIG. 11 illustrates an example computing environment with an examplecomputer device suitable for use in some example implementations, suchas a change over detection server coupled to a network connected toenvironmental devices and another network connected to the PLC and a MESas illustrated in FIG. 1, or a smart PLC configured to manage the PLC asillustrated in FIG. 10.

Computer device 1105 in computing environment 1100 can include one ormore processing units, cores, or processors 1110, memory 1115 (e.g.,RAM, ROM, and/or the like), internal storage 1120 (e.g., magnetic,optical, solid state storage, and/or organic), and/or 10 interface 1125,any of which can be coupled on a communication mechanism or bus 1130 forcommunicating information or embedded in the computer device 1105. 10interface 1125 is also configured to receive images from cameras orprovide images to projectors or displays, depending on the desiredimplementation.

Computer device 1105 can be communicatively coupled to input/userinterface 1135 and output device/interface 1140. Either one or both ofinput/user interface 1135 and output device/interface 1140 can be awired or wireless interface and can be detachable. Input/user interface1135 may include any device, component, sensor, or interface, physicalor virtual, that can be used to provide input (e.g., buttons,touch-screen interface, keyboard, a pointing/cursor control, microphone,camera, braille, motion sensor, optical reader, and/or the like). Outputdevice/interface 1140 may include a display, television, monitor,printer, speaker, braille, or the like. In some example implementations,input/user interface 1135 and output device/interface 1140 can beembedded with or physically coupled to the computer device 1105. Inother example implementations, other computer devices may function as orprovide the functions of input/user interface 1135 and outputdevice/interface 1140 for a computer device 1105.

Examples of computer device 1105 may include, but are not limited to,highly mobile devices (e.g., smartphones, devices in vehicles and othermachines, devices carried by humans and animals, and the like), mobiledevices (e.g., tablets, notebooks, laptops, personal computers, portabletelevisions, radios, and the like), and devices not designed formobility (e.g., desktop computers, other computers, information kiosks,televisions with one or more processors embedded therein and/or coupledthereto, radios, and the like).

Computer device 1105 can be communicatively coupled (e.g., via IOinterface 1125) to external storage 1145 and network 1150 forcommunicating with any number of networked components, devices, andsystems, including one or more computer devices of the same or differentconfiguration. Computer device 1105 or any connected computer device canbe functioning as, providing services of, or referred to as a server,client, thin server, general machine, special-purpose machine, oranother label.

IO interface 1125 can include, but is not limited to, wired and/orwireless interfaces using any communication or IO protocols or standards(e.g., Ethernet, 802.11x, Universal System Bus, WiMax, modem, a cellularnetwork protocol, and the like) for communicating information to and/orfrom at least all the connected components, devices, and network incomputing environment 1100. Network 1150 can be any network orcombination of networks (e.g., the Internet, local area network, widearea network, a telephonic network, a cellular network, satellitenetwork, and the like).

Computer device 1105 can use and/or communicate using computer-usable orcomputer-readable media, including transitory media and non-transitorymedia. Transitory media include transmission media (e.g., metal cables,fiber optics), signals, carrier waves, and the like. Non-transitorymedia include magnetic media (e.g., disks and tapes), optical media(e.g., CD ROM, digital video disks, Blu-ray disks), solid state media(e.g., RAM, ROM, flash memory, solid-state storage), and othernon-volatile storage or memory.

Computer device 1105 can be used to implement techniques, methods,applications, processes, or computer-executable instructions in someexample computing environments. Computer-executable instructions can beretrieved from transitory media, and stored on and retrieved fromnon-transitory media. The executable instructions can originate from oneor more of any programming, scripting, and machine languages (e.g., C,C++, C#, Java, Visual Basic, Python, Perl, JavaScript, and others).

Processor(s) 1110 can execute under any operating system (OS) (notshown), in a native or virtual environment. One or more applications canbe deployed that include logic unit 1160, application programminginterface (API) unit 1165, input unit 1170, output unit 1175, andinter-unit communication mechanism 1195 for the different units tocommunicate with each other, with the OS, and with other applications(not shown). The described units and elements can be varied in design,function, configuration, or implementation and are not limited to thedescriptions provided. Processor(s) 1110 can be in the form of hardwareprocessors such as central processing units (CPUs) or in a combinationof hardware and software units.

In some example implementations, when information or an executioninstruction is received by API unit 1165, it may be communicated to oneor more other units (e.g., logic unit 1160, input unit 1170, output unit1175). In some instances, logic unit 1160 may be configured to controlthe information flow among the units and direct the services provided byAPI unit 1165, input unit 1170, output unit 1175, in some exampleimplementations described above. For example, the flow of one or moreprocesses or implementations may be controlled by logic unit 1160 aloneor in conjunction with API unit 1165. The input unit 1170 may beconfigured to obtain input for the calculations described in the exampleimplementations, and the output unit 1175 may be configured to provideoutput based on the calculations described in example implementations.

Processor(s) 1110 can be configured to, for a detection of a stoppageand a restart of the PLC within a threshold period of time, retrievefirst metrics from the PLC from before the stoppage and second metricsfrom the PLC from after the restart; extract first features from thefirst metrics and second features from the second metrics; and for adifference between the first features and the second features exceedinga threshold, transmit a notification to an asset management serverindicative of a changeover of an asset of the PLC as illustrated in FIG.6(a).

As illustrated in FIG. 6(b), the first features can involve a pluralityof different types of features, wherein the second features involves theplurality of different types of features, wherein the difference isdetermined based on differences across the plurality of different typesof features between the first features and the second features over aperiod of time. Depending on the desired implementation, the differencecan also be determined based on a number of differences across theplurality of different types of features between the first features andthe second features that exceed a predetermined threshold for eachmanaged PLC as illustrated in FIGS. 6(a) and 6(b).

As illustrated in FIG. 9 through the data indicated in FIG. 7,processor(s) 1110 can be configured to retrieve, from operation signalsof the PLC, series data corresponding to the PLC; and for a score of achangeover detection model executed on the series data of the PLCexceeding another threshold, transmitting the notification to an assetmanagement server indicative of a changeover of an asset of the PLC.

As illustrated in FIG. 8(a), processor(s) 1110 can be configured toapply reinforcement learning on the changeover detection model based ona portion of the series data corresponding to the PLC before thenotification indicative of changeover of the asset, with indicationsregarding whether a change over has occurred or not.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations within a computer.These algorithmic descriptions and symbolic representations are themeans used by those skilled in the data processing arts to convey theessence of their innovations to others skilled in the art. An algorithmis a series of defined steps leading to a desired end state or result.In example implementations, the steps carried out require physicalmanipulations of tangible quantities for achieving a tangible result.

Unless specifically stated otherwise, as apparent from the discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing,” “computing,” “calculating,” “determining,”“displaying,” or the like, can include the actions and processes of acomputer system or other information processing device that manipulatesand transforms data represented as physical (electronic) quantitieswithin the computer system's registers and memories into other datasimilarly represented as physical quantities within the computersystem's memories or registers or other information storage,transmission or display devices.

Example implementations may also relate to an apparatus for performingthe operations herein. This apparatus may be specially constructed forthe required purposes, or it may include one or more general-purposecomputers selectively activated or reconfigured by one or more computerprograms. Such computer programs may be stored in a computer readablemedium, such as a computer-readable storage medium or acomputer-readable signal medium. A computer-readable storage medium mayinvolve tangible mediums such as, but not limited to optical disks,magnetic disks, read-only memories, random access memories, solid statedevices and drives, or any other types of tangible or non-transitorymedia suitable for storing electronic information. A computer readablesignal medium may include mediums such as carrier waves. The algorithmsand displays presented herein are not inherently related to anyparticular computer or other apparatus. Computer programs can involvepure software implementations that involve instructions that perform theoperations of the desired implementation.

Various general-purpose systems may be used with programs and modules inaccordance with the examples herein, or it may prove convenient toconstruct a more specialized apparatus to perform desired method steps.In addition, the example implementations are not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement theteachings of the example implementations as described herein. Theinstructions of the programming language(s) may be executed by one ormore processing devices, e.g., central processing units (CPUs),processors, or controllers.

As is known in the art, the operations described above can be performedby hardware, software, or some combination of software and hardware.Various aspects of the example implementations may be implemented usingcircuits and logic devices (hardware), while other aspects may beimplemented using instructions stored on a machine-readable medium(software), which if executed by a processor, would cause the processorto perform a method to carry out implementations of the presentapplication. Further, some example implementations of the presentapplication may be performed solely in hardware, whereas other exampleimplementations may be performed solely in software. Moreover, thevarious functions described can be performed in a single unit, or can bespread across a number of components in any number of ways. Whenperformed by software, the methods may be executed by a processor, suchas a general purpose computer, based on instructions stored on acomputer-readable medium. If desired, the instructions can be stored onthe medium in a compressed and/or encrypted format.

Moreover, other implementations of the present application will beapparent to those skilled in the art from consideration of thespecification and practice of the teachings of the present application.Various aspects and/or components of the described exampleimplementations may be used singly or in any combination. It is intendedthat the specification and example implementations be considered asexamples only, with the true scope and spirit of the present applicationbeing indicated by the following claims.

What is claimed is:
 1. A method, comprising: for a detection of astoppage and a restart of the PLC within a threshold period of time:retrieving first metrics from the PLC from before the stoppage andsecond metrics from the PLC from after the restart; extracting firstfeatures from the first metrics and second features from the secondmetrics; and for a difference between the first features and the secondfeatures exceeding a threshold, transmitting a notification to an assetmanagement server indicative of a changeover of an asset of the PLC. 2.The method of claim 1, wherein the first features comprises a pluralityof different types of features, wherein the second features comprisesthe plurality of different types of features, wherein the difference isdetermined based on differences across the plurality of different typesof features between the first features and the second features over aperiod of time.
 3. The method of claim 1, wherein the first featurescomprises a plurality of different types of features, wherein the secondfeatures comprises the plurality of different types of features, whereinthe difference is determined based on a number of differences across theplurality of different types of features between the first features andthe second features that exceed a predetermined threshold for eachmanaged PLC.
 4. The method of claim 1, further comprising: retrieving,from operation signals of the PLC, series data corresponding to the PLC;and for a score of a changeover detection model executed on the seriesdata of the PLC exceeding another threshold, transmitting thenotification to an asset management server indicative of a changeover ofan asset of the PLC.
 5. The method of claim 4, further comprisingapplying reinforcement learning on the changeover detection model basedon a portion of the series data corresponding to the PLC before thenotification indicative of changeover of the asset, with indicationsregarding whether a change over has occurred or not.
 6. The method ofclaim 1, wherein the method is executed by a server communicativelycoupled to a first network connected to environmental devices and asecond network connected to the PLC and a Manufacturing Execution System(MES).
 7. The method of claim 1, wherein the method is executed by asmart PLC configured to manage the PLC.
 8. A non-transitory computerreadable medium, storing instructions for executing a process, theinstructions comprising: for a detection of a stoppage and a restart ofthe PLC within a threshold period of time: retrieving first metrics fromthe PLC from before the stoppage and second metrics from the PLC fromafter the restart; extracting first features from the first metrics andsecond features from the second metrics; and for a difference betweenthe first features and the second features exceeding a threshold,transmitting a notification to an asset management server indicative ofa changeover of an asset of the PLC.
 9. The non-transitory computerreadable medium of claim 8, wherein the first features comprises aplurality of different types of features, wherein the second featurescomprises the plurality of different types of features, wherein thedifference is determined based on differences across the plurality ofdifferent types of features between the first features and the secondfeatures over a period of time.
 10. The non-transitory computer readablemedium of claim 8, wherein the first features comprises a plurality ofdifferent types of features, wherein the second features comprises theplurality of different types of features, wherein the difference isdetermined based on a number of differences across the plurality ofdifferent types of features between the first features and the secondfeatures that exceed a predetermined threshold for each managed PLC. 11.The non-transitory computer readable medium of claim 8, the instructionsfurther comprising: retrieving, from operation signals of the PLC,series data corresponding to the PLC; and for a score of a changeoverdetection model executed on the series data of the PLC exceeding anotherthreshold, transmitting the notification to an asset management serverindicative of a changeover of an asset of the PLC.
 12. Thenon-transitory computer readable medium of claim 11, further comprisingapplying reinforcement learning on the changeover detection model basedon a portion of the series data corresponding to the PLC before thenotification indicative of changeover of the asset, with indicationsregarding whether a change over has occurred or not.
 13. Thenon-transitory computer readable medium of claim 8, wherein theinstructions are executed by a server communicatively coupled to a firstnetwork connected to environmental devices and a second networkconnected to the PLC and a Manufacturing Execution System (MES).
 14. Thenon-transitory computer readable medium of claim 8, wherein theinstructions are executed by a smart PLC configured to manage the PLC.15. An apparatus, comprising: a processor, configured to, for adetection of a stoppage and a restart of the PLC within a thresholdperiod of time: retrieve first metrics from the PLC from before thestoppage and second metrics from the PLC from after the restart; extractfirst features from the first metrics and second features from thesecond metrics; and for a difference between the first features and thesecond features exceeding a threshold, transmit a notification to anasset management server indicative of a changeover of an asset of thePLC.